code: def average_treatment_effect_testsuite(
      self,
      tests_to_run="all",
      num_common_causes=[2, 3],
      num_instruments=[
          1,
      ],
      num_effect_modifiers=[
          0,
      ],
      num_treatments=[
          1,
      ],
      num_frontdoor_variables=[
          0,
      ],
      treatment_is_binary=[
          True,
      ],
      treatment_is_category=[
          False,
      ],
      outcome_is_binary=[
          False,
      ],
      confidence_intervals=[
          False,
      ],
      test_significance=[
          False,
      ],
      dataset="linear",
      method_params=None,
  ):
      args_dict = {
          "num_common_causes": num_common_causes,
          "num_instruments": num_instruments,
          "num_effect_modifiers": num_effect_modifiers,
          "num_treatments": num_treatments,
          "num_frontdoor_variables": num_frontdoor_variables,
          "treatment_is_binary": treatment_is_binary,
          "treatment_is_category": treatment_is_category,
          "outcome_is_binary": outcome_is_binary,
          "confidence_intervals": confidence_intervals,
          "test_significance": test_significance,
      }
      keys, values = zip(*args_dict.items())
      configs = [dict(zip(keys, v)) for v in itertools.product(*values)]
      for cfg in configs:
          print("\nConfig:", cfg)
          cfg["dataset"] = dataset
          cfg["method_params"] = method_params
          self.average_treatment_effect_test(**cfg)
  
file_path: dowhy/tests/causal_estimators/base.py
imports: import itertools
  import dowhy.datasets
  from dowhy import EstimandType, identify_effect_auto
  from dowhy.graph import build_graph_from_str
label_desc: Use image node_id 3 for calling the SimpleEstimator obj's underlying member method code with example usage: obj.average_treatment_effect_testsuite(tests_to_run, num_common_causes, num_instruments, num_effect_modifiers, num_treatments, num_frontdoor_variables, treatment_is_binary, treatment_is_category, outcome_is_binary, confidence_intervals, test_significance, dataset, method_params) without return types